New decoding algorithms for Hidden Markov Models using distance measures on labellings

<p>Abstract</p> <p>Background</p> <p>Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries.</p> <p>Results</p> <p>We give a set of algorithms to compute the conditional probability of all labellings "near" a reference labelling <it>λ </it>for a sequence <it>y </it>for a variety of definitions of "near". In addition, we give optimization algorithms to find the best labelling for a sequence in the robust sense of having all of its feature boundaries nearly correct. Natural problems in this domain are <it>NP</it>-hard to optimize. For membrane proteins, our algorithms find the approximate topology of such proteins with comparable success to existing programs, while being substantially more accurate in estimating the positions of transmembrane helix boundaries.</p> <p>Conclusion</p> <p>More robust HMM decoding may allow for better analysis of sequence features, in reasonable runtimes.</p

Optical response of supported gold nanodisks

It is shown that the ellipsometric spectra of short range ordered planar arrays of gold nanodisks supported on glass substrates can be described by modeling the nanostructured arrays as uniaxial homogeneous layers with dielectric functions of the Lorentz type. However, appreciable deviations from experimental data are observed in calculated spectra of irradiance measurements. A qualitative and quantitative description of all measured spectra is obtained with a uniaxial effective medium dielectric function in which the nanodisks are modeled as oblate spheroids. Dynamic depolarization factors in the long-wavelength approximation and interaction with the substrate are considered. Similar results are obtained calculating the optical spectra using the island-film theory. Nevertheless, a small in-plane anisotropy and quadrupolar coupling effects reveal a very complex optical response of the nanostructured arrays

Systematics of c-axis Phonons in the Thallium and Bismuth Based Cuprate Superconductors

We present grazing incidence reflectivity measurements in the far infrared region at temperatures above and below Tc for a series of thallium (Tl2Ba2CuO6, Tl2Ba2CaCu2O8) and bismuth (Bi2Sr2CuO6, Bi2Sr2CaCu2O8, and Bi(2-x)Pb(x)Sr2CaCu2O8) based cuprate superconductors. From the spectra, which are dominated by the c-axis phonons, longitudinal frequencies (LO) are directly obtained. The reflectivity curves are well fitted by a series of Lorentz oscillators. In this way the transverse (TO) phonon frequencies were accurately determined. On the basis of the comparative study of the Bi and Tl based cuprates with different number of CuO2 layers per unit cell, we suggest modifications of the assignment of the main oxygen modes. We compare the LO frequencies in Bi2Sr2CaCu2O8 and Tl2Ba2Ca2Cu3O10 obtained from intrinsic Josephson junction characteristics with our measurements, and explain the discrepancy in LO frequencies obtained by the two different methods.Comment: 8 pages Revtex, 6 eps figures, 3 tables, to appear in Phys. Rev.

Randomized assessment of imatinib in patients with acute ischaemic stroke treated with intravenous thrombolysis

BackgroundImatinib, a tyrosine kinase inhibitor, has been shown to restore bloodâ brain barrier integrity and reduce infarct size, haemorrhagic transformation and cerebral oedema in stroke models treated with tissue plasminogen activator. We evaluated the safety of imatinib, based on clinical and neuroradiological data, and its potential influence on neurological and functional outcomes.MethodsA phase II randomized trial was performed in patients with acute ischaemic stroke treated with intravenous thrombolysis. A total of 60 patients were randomly assigned to four groups [3 (active): 1 (control)]; the active treatment groups received oral imatinib for 6 days at three dose levels (400, 600 and 800 mg). Primary outcome was any adverse event; secondary outcomes were haemorrhagic transformation, cerebral oedema, neurological severity on the National Institutes of Health Stroke Scale (NIHSS) at 7 days and at 3 months and functional outcomes on the modified Rankin scale (mRS).ResultsFour serious adverse events were reported, which resulted in three deaths (one in the control group and two in the 400â mg dose group; one patient in the latter group did not receive active treatment and the other received two doses). Nonserious adverse events were mostly mild, resulting in full recovery. Imatinib ameliorated neurological outcomes with an improvement of 0.6 NIHSS points per 100 mg imatinib (P = 0.02). For the 800â mg group, the mean unadjusted and adjusted NIHSS improvements were 4 (P = 0.037) and 5 points (P = 0.012), respectively, versus controls. Functional independence (mRS 0â 2) increased by 18% versus controls (61 vs. 79; P = 0.296).ConclusionThis phase II study showed that imatinib is safe and tolerable and may reduce neurological disability in patients treated with intravenous thrombolysis after ischaemic stroke. A confirmatory randomized trial is currently underway.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136298/1/joim12576_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136298/2/joim12576.pd

Cellular expression, trafficking, and function of two isoforms of human ULBP5/RAET1G

Background: The activating immunoreceptor NKG2D is expressed on Natural Killer (NK) cells and subsets of T cells. NKG2D contributes to anti-tumour and anti-viral immune responses in vitro and in vivo. The ligands for NKG2D in humans are diverse proteins of the MIC and ULBP/RAET families that are upregulated on the surface of virally infected cells and tumours. Two splicing variants of ULBP5/RAET1G have been cloned previously, but not extensively characterised. Methodology/Principal Findings: We pursue a number of approaches to characterise the expression, trafficking, and function of the two isoforms of ULBP5/RAET1G. We show that both transcripts are frequently expressed in cell lines derived from epithelial cancers, and in primary breast cancers. The full-length transcript, RAET1G1, is predicted to encode a molecule with transmembrane and cytoplasmic domains that are unique amongst NKG2D ligands. Using specific anti-RAET1G1 antiserum to stain tissue microarrays we show that RAET1G1 expression is highly restricted in normal tissues. RAET1G1 was expressed at a low level in normal gastrointestinal epithelial cells in a similar pattern to MICA. Both RAET1G1 and MICA showed increased expression in the gut of patients with celiac disease. In contrast to healthy tissues the RAET1G1 antiserum stained a wide variety or different primary tumour sections. Both endogenously expressed and transfected RAET1G1 was mainly found inside the cell, with a minority of the protein reaching the cell surface. Conversely the truncated splicing variant of RAET1G2 was shown to encode a soluble molecule that could be secreted from cells. Secreted RAET1G2 was shown to downregulate NKG2D receptor expression on NK cells and hence may represent a novel tumour immune evasion strategy. Conclusions/Significance: We demonstrate that the expression patterns of ULBP5RAET1G are very similar to the well-characterised NKG2D ligand, MICA. However the two isoforms of ULBP5/RAET1G have very different cellular localisations that are likely to reflect unique functionality

Integrating sequence and structural biology with DAS.

BACKGROUND: The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence. RESULTS: Here we present several extensions to the current DAS 1.5 protocol. These provide new commands to share alignments, three dimensional molecular structure data, add the possibility for registration and discovery of DAS servers, and provide a convention how to provide different types of data plots. We present examples of web sites and applications that use the new extensions. We operate a public registry of DAS sources, which now includes entries for more than 250 distinct sources. CONCLUSION: Our DAS extensions are essential for the management of the growing number of services and exchange of diverse biological data sets. In addition the extensions allow new types of applications to be developed and scientific questions to be addressed. The registry of DAS sources is available at http://www.dasregistry.org.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Transmembrane protein topology prediction using support vector machines

Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins

Electronic Raman-scattering study of low-energy excitations in single and double CuO_2-layer Tl-Ba-(Ca)-Cu-O superconductors

Low energy Raman continuum and the redistribution of the continuum to a peak (2\Delta-peak) in the superconducting state have been studied in Tl-Ba-(Ca)-Cu-O superconductors with single (Tl-2201) and double (Tl-2212) CuO_2 layer. The 2\Delta/k_BT_c ratios in A_1g and B_1g symmetries are larger for Tl-2212 than for Tl-2201. The B_1g/A_1g gap ratio is also larger in Tl-2212. The A_1g intensities of the continuum and the 2\Delta-peak are significantly weaker than the B_1g intensities in Tl-2201, but are comparable in Tl-2212. This shows that the Coulomb screening is much stronger in Tl-2201. The change from Tl-2201 to Tl-2212 of the normalized A_1g 2\Delta-peak intensity is identical within experimental error to that of normalized A_1g continuum intensity. This suggests that the excitations forming the 2\Delta-peak and the continuum couple to light by the same mechanism.Comment: 4 pages, PDF forma

Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

<p>Abstract</p> <p>Background</p> <p>Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing <it>semi-supervised methods</it> for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data.</p> <p>Results</p> <p>In this paper, we present an Abstraction Augmented Markov Model (AAMM) based approach to semi-supervised protein subcellular localization prediction problem. We investigate the effectiveness of AAMMs in exploiting <it>unlabeled</it> data. We compare semi-supervised AAMMs with: (i) Markov models (MMs) (which do not take advantage of unlabeled data); (ii) an expectation maximization (EM); and (iii) a co-training based approaches to semi-supervised training of MMs (that make use of unlabeled data).</p> <p>Conclusions</p> <p>The results of our experiments on three protein subcellular localization data sets show that semi-supervised AAMMs: (i) can effectively exploit unlabeled data; (ii) are more accurate than both the MMs and the EM based semi-supervised MMs; and (iii) are comparable in performance, and in some cases outperform, the co-training based semi-supervised MMs.</p

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/